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Objective <p>China ranks third globally in tuberculosis burden, with Xinjiang being one of the most severely affected regions. Evaluating environmental drivers (e.g., meteorological conditions, air quality) is vital for developing localized strategies to reduce tuberculosis prevalence.</p> Methods <p>Age-standardized incidence rates (ASR) and estimated annual percentage changes (EAPC) quantified global trends. Joinpoint regression analyzed temporal trends in China and Xinjiang, while spatial autocorrelation examined regional patterns. A spatiotemporal Bayesian hierarchical model (INLA-SPDE algorithm) assessed environmental impacts on tuberculosis incidence across 14 Xinjiang prefectures (2010-2022).</p> Results <p>In 2021, India had the highest ASR (214.39/100,000), while China’s ASR was 36.28/100,000. The Philippines showed the steepest ASR increase (EAPC 0.82, 95% CI 0.50-1.14), whereas China’s ASR declined significantly (EAPC −3.78, 95% CI −3.67 to&#xa0;−3.90). Xinjiang demonstrated a steeper annual reduction (27.15%, 2018-2022). Spatial clustering was significant (Moran’s I&#xa0;&gt; 0.5, <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13662_2025_3994_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="66" /> </InlineMediaObject> <EquationSource Format="MATHML"><math> <mi>P</mi> <mo>&lt;</mo> <mn>0.01</mn> </math></EquationSource> <EquationSource Format="TEX">$P &lt; 0.01$</EquationSource> </InlineEquation>), with an 897.57 km correlation distance. Air pollutants (PM2.5, PM10, CO) positively correlated with incidence, while temperature and wind speed were inversely associated.</p> Conclusions <p>Regional variations in environmental impacts necessitate location-specific tuberculosis prevention strategies. Targeted surveillance and resource allocation in high-burden areas are critical for advancing global tuberculosis elimination.</p>

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Spatiotemporal dynamics of tuberculosis in Xinjiang, China: unraveling the roles of meteorological conditions and air pollution via hierarchical Bayesian modeling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月30日

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  • Feifei Li,
  • Yaoqin Lu,
  • Peiyao Zhou,
  • Yanling Zheng,
  • Liping Zhang

摘要

Objective

China ranks third globally in tuberculosis burden, with Xinjiang being one of the most severely affected regions. Evaluating environmental drivers (e.g., meteorological conditions, air quality) is vital for developing localized strategies to reduce tuberculosis prevalence.

Methods

Age-standardized incidence rates (ASR) and estimated annual percentage changes (EAPC) quantified global trends. Joinpoint regression analyzed temporal trends in China and Xinjiang, while spatial autocorrelation examined regional patterns. A spatiotemporal Bayesian hierarchical model (INLA-SPDE algorithm) assessed environmental impacts on tuberculosis incidence across 14 Xinjiang prefectures (2010-2022).

Results

In 2021, India had the highest ASR (214.39/100,000), while China’s ASR was 36.28/100,000. The Philippines showed the steepest ASR increase (EAPC 0.82, 95% CI 0.50-1.14), whereas China’s ASR declined significantly (EAPC −3.78, 95% CI −3.67 to −3.90). Xinjiang demonstrated a steeper annual reduction (27.15%, 2018-2022). Spatial clustering was significant (Moran’s I > 0.5, P < 0.01 $P < 0.01$ ), with an 897.57 km correlation distance. Air pollutants (PM2.5, PM10, CO) positively correlated with incidence, while temperature and wind speed were inversely associated.

Conclusions

Regional variations in environmental impacts necessitate location-specific tuberculosis prevention strategies. Targeted surveillance and resource allocation in high-burden areas are critical for advancing global tuberculosis elimination.